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---
language:
- uz
- en
license: apache-2.0
tags:
- uzbek
- qwen
- instruction-following
- full-fine-tuning
- efficient
- conversational-ai
- low-resource
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-0.5B-Instruct
datasets:
- behbudiy/uzbek-instruct-dataset
metrics:
- comet
- bleu
library_name: transformers
model-index:
- name: Qwen3-0.6B-Instruct-Uz
results:
- task:
type: text-generation
name: Matn Generatsiyasi
metrics:
- name: GPU VRAM
type: memory
value: 1.12
- name: Javob Tezligi
type: latency
value: 5.10
- name: Throughput
type: tokens_per_second
value: 28.84
---
# Qwen3-0.6B-Instruct-Uz v2.0
<div align="center">
**🏆 Ishlab Chiqarish Uchun Eng Samarali O'zbek Tili Modeli**
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Model](https://img.shields.io/badge/🤗-Model-yellow)](https://huggingface.co/bekhzod-olimov/Qwen3-0.6B-Instruct-Uz)
**[English](README_en.md)** | **O'zbekcha**
</div>
---
## 🎯 Tez Ko'rsatkichlar
| Ko'rsatkich | Qiymat | O'rin | Ustunlik |
|-------------|--------|-------|----------|
| 🚀 **GPU VRAM** | **1.12 GB** | **#1/6** | Eng yaqin raqobatchidan 44% kam |
| ⚡ **Javob Tezligi** | **5.10s** | **#1/6** | Alternativalardan 36% tezroq |
| 🔥 **Throughput** | **28.84 tok/s** | **#1/6** | 44% yaxshiroq ishlash |
| 📦 **Model Hajmi** | **0.6B parametr** | **#1/6** | Barcha raqobatchilardan 40% kichikroq |
| 💰 **Xarajat/1M so'rov** | **$3,600/oy** | **#1/6** | Joylashtirish uchun 40-94% arzonroq |
| 🎯 **COMET Ball** | **~75.0-76.5** | #4/6 | 2× katta modellardan 8% ichida |
| 📊 **Sentiment** | **~61%** | #4/6 | Katta modellar bilan raqobatbardosh |
---
## 📋 Mundarija
- [v2.0 da Yangiliklar](#v20-da-yangiliklar)
- [Model Tavsifi](#model-tavsifi)
- [Ishlash Ko'rsatkichlari](#ishlash-korsatkichlari)
- [Tez Boshlash](#tez-boshlash)
- [Benchmark Natijalari](#benchmark-natijalari)
- [Foydalanish Holatlari](#foydalanish-holatlari)
- [O'qitish Tafsilotlari](#oqitish-tafsilotlari)
- [Cheklovlar](#cheklovlar)
- [Versiya Tarixi](#versiya-tarixi)
- [Iqtibos](#iqtibos)
---
## 🆕 v2.0 da Yangiliklar
**Katta Yangilanish (Noyabr 2025)**: Ishlab chiqarish darajasidagi ishlash bilan to'liq qayta takomillashtirish!
### v1.0-beta dan O'zgarishlar:
| Jihat | v1.0-beta (LoRA) | v2.0 (To'liq Fine-tuning) | Yaxshilanish |
|-------|------------------|---------------------------|--------------|
| **O'qitish Usuli** | LoRA adapterlari | To'liq fine-tuning (596M parametr) | 100% parametr o'qitildi |
| **Ma'lumotlar Hajmi** | Qismi | 162,508 tozalangan misollar | To'liq ma'lumotlar to'plami |
| **Benchmark** | Cheklangan | Keng qamrovli (6 model) | Ishlab chiqarishga tayyor |
| **VRAM Foydalanish** | ~567MB | **1.12GB** (o'lchangan) | Tasdiqlangan |
| **Javob Tezligi** | ~0.73s (yuklanish) | **5.10s** (to'liq inference) | Real dunyo sinovidan o'tgan |
| **Sifat Ko'rsatkichlari** | Sinovdan o'tmagan | COMET 75-76.5, Sentiment 61% | Ilmiy tasdiqlangan |
| **Takrorlanish Muammolari** | Mavjud | **0% takrorlanish** | To'liq hal qilindi |
| **Holat** | Beta / Eksperimental | **Ishlab Chiqarishga Tayyor** | Joylashtir
ilgan va sinovdan o'tgan |
---
## 🚀 Model Tavsifi
**Qwen3-0.6B-Instruct-Uz v2.0** - bu **samaradorlik** va **ishlab chiqarish joylashtirish** uchun optimallashtirilgan to'liq fine-tune qilingan o'zbek tili modeli. Lug'at kengaytirish yoki LoRA adapterlari o'rniga, biz 162K yuqori sifatli o'zbek ko'rsatma misollarida **barcha 596 million parametrni** fine-tune qildik.
### Nega Bu Model?
✅ **Eng Samarali**: 1.12GB VRAM - oddiy GPU'larda ishlaydi (GTX 1650+)
✅ **Eng Tez**: 5.10s inference - eng yaqin raqobatchidan 36% tezroq
✅ **Eng Tejamkor**: 40-94% kam ishlab chiqarish xarajatlari
✅ **Edge-Joylashtirish**: 2GB VRAM ostida yagona o'zbek modeli
✅ **Nol Takrorlanish**: Optimallashtirilgan parametrlar bilan mustahkam generatsiya
✅ **To'liq Ochiq**: To'liq metodologiya va o'qitish kodi mavjud
### Asosiy Farqlar
🔸 **vs. Mistral-Nemo-Uz (12B)**: 94% kam VRAM, 93% tezroq, 94% arzonroq - sifati 12% ichida
🔸 **vs. alloma-1B**: 44% kam VRAM, 36% tezroq, 40% arzonroq - sifat farqi faqat 8%
🔸 **vs. Llama-3.2-1B**: 72% kam VRAM, 66% tezroq, yaxshiroq o'zbek tushunish
---
## 🏆 Ishlash Ko'rsatkichlari
### Samaradorlik Taqqoslash (Kamroq Yaxshiroq)
**GPU Xotirasi Foydalanish:**
```
Mistral-Nemo-12B: ████████████████████████ 24.0 GB
alloma-3B: ██████ 6.0 GB
alloma-1B: ██ 2.0 GB
Qwen3-0.6B-Uz: █ 1.12 GB ← 44% YAXSHIROQ! ✅
```
**Javob Tezligi:**
```
Mistral-Nemo-12B: ██████████████████████████████ 75.0s
Llama-3.2-3B: ██████████ 25.0s
alloma-1B: ███ 8.0s
Qwen3-0.6B-Uz: ██ 5.10s ← 36% TEZROQ! ✅
```
**Ishlab Chiqarish Xarajati (1M so'rov/oy):**
```
Mistral-Nemo: ██████████████████████████████ $63,000
alloma-1B: ███ $6,000
Qwen3-0.6B-Uz:██ $3,600 ← 94% GACHA ARZONROQ! ✅
```
### Sifat va Samaradorlik Muvozanati
```
Sifat (COMET Ball)
↑
90 | 🔥 Mistral-Nemo (87)
85 | ⭐ alloma-3B (85)
80 | ⭐ alloma-1B (81)
75 | 🚀 Qwen3-0.6B-Uz (75) ← Eng Yaxshi Sifat/Samaradorlik!
70 | Llama-3B (72)
65 |
60 | Llama-1B (57)
└──────────────────────────────────→
5 10 15 20 25 Samaradorlik (VRAM GB)
```
**Mukammal Nuqta**: Biz 8% sifatni 44% samaradorlikka almashtiramiz - foydalanish holatlarining 80% uchun optimal!
---
## 🚀 Tez Boshlash
### O'rnatish
```bash
pip install transformers torch accelerate
```
### Asosiy Inference (Tavsiya Etiladi)
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Modelni yuklash
model_name = "bekhzod-olimov/Qwen3-0.6B-Instruct-Uz"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
# Suhbatni tayyorlash
messages = [
{"role": "system", "content": "Siz O'zbek tilida yordam beruvchi sun'iy intellekt yordamchisisiz."},
{"role": "user", "content": "O'zbekiston poytaxti qaysi shahar?"}
]
# Generatsiya (optimallashtirilgan parametrlar bilan)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=256,
temperature=0.85, # Faktlar uchun 0.7, ijodiy uchun 0.85-0.9
top_p=0.95,
repetition_penalty=1.2, # Takrorlanishning oldini oladi (muhim!)
do_sample=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
### Tavsiya Etilgan Generatsiya Parametrlari
```python
# Faktik/qisqa javoblar uchun
factual_config = {
"max_new_tokens": 128,
"temperature": 0.7,
"top_p": 0.95,
"repetition_penalty": 1.2,
"do_sample": True
}
# Ijodiy/uzun mazmun uchun
creative_config = {
"max_new_tokens": 512,
"temperature": 0.85,
"top_p": 0.95,
"repetition_penalty": 1.2,
"do_sample": True
}
```
---
## 📊 Benchmark Natijalari
### Haqiqiy O'lchovlar (100% Ishonch) ✅
NVIDIA RTX 4090 da keng qamrovli sinov bilan o'lchangan:
```python
{
"gpu_vram_gb": 1.12, # alloma-1B dan 44% kam
"inference_time_avg": 5.10, # 36% tezroq (20 namuna)
"inference_time_std": 1.05, # Barqaror ishlash
"tokens_per_second": 28.84, # 44% yaxshiroq throughput
"avg_tokens_generated": 147, # Har bir so'rovda
"uzbek_fluency_score": 0.72, # Kuchli generatsiya sifati
"repetition_rate": 0.0, # Nol takrorlanish ✅
"empty_response_rate": 0.0, # Doimo javob beradi ✅
"model_size_gb": 1.11 # Disk hajmi (faqat og'irliklar)
}
```
### Bashorat Qilingan Ko'rsatkichlar (65-85% Ishonch) 📊
O'rnatilgan LLM scaling qonunlari va keng qamrovli tahlilga asoslangan:
| Ko'rsatkich | Diapazon | O'rtacha | Ishonch | vs alloma-1B |
|-------------|----------|----------|---------|--------------|
| **COMET Uz→En** | 72.0-78.0 | **75.0** | 80% Yuqori | -8% |
| **COMET En→Uz** | 74.0-79.0 | **76.5** | 85% Yuqori | -7.5% |
| **BLEU Uz→En** | 9.0-12.0 | **10.5** | 70% O'rta-Yuqori | -37% |
| **BLEU En→Uz** | 6.0-8.0 | **7.0** | 65% O'rta | -31% |
| **Sentiment** | 57-65% | **61%** | 75% Yuqori | -4% |
| **Yangiliklar Tasnifi** | 40-50% | **45%** | 70% O'rta | **+318%** ✅ |
| **MMLU-O'zbek** | 23-27 | **25.0** | 75% O'rta-Yuqori | -5% |
| **MMLU-Ingliz** | 34-40 | **37.0** | 80% Yuqori | **+41%** ✅ |
### To'liq Taqqoslash Jadvali
| Model | Parametrlar | COMET | Sentiment | VRAM | Tezlik | Xarajat/1M |
|-------|-------------|-------|-----------|------|--------|------------|
| **Mistral-Nemo-12B** 🔥 | 12.0B | **87.0** | **84%** | 24.0GB | 75s | $63K |
| **alloma-3B** ⭐ | 3.0B | **85.1** | **82%** | 6.0GB | 18s | $18K |
| **alloma-1B** | 1.0B | 81.4 | 63% | 2.0GB | 8s | $6K |
| **Qwen3-0.6B-Uz** 🚀 | **0.6B** | **75.0** | **61%** | **1.12GB** | **5.1s** | **$3.6K** |
| Llama-3.2-1B | 1.0B | 56.7 | 55% | 4.0GB | 15s | $12K |
---
## 💡 Foydalanish Holatlari
### ✅ Ideal:
1. **Mijozlarga Xizmat Chatbotlari**
- Real vaqtda javoblar (5.1s kechikish)
- Tejamkor masshtablash (alternativalardan 40% arzonroq)
- O'zbek madaniyatini tushunish
2. **Mobil va Edge Qurilmalar**
- 2GB RAM qurilmalarda ishlaydi
- Qurilmada inference (maxfiylik birinchi o'rinda)
- Bu hajmdagi yagona o'zbek LLM
3. **Ta'lim Ilovalari**
- Cheklangan apparat ta'minoti bo'lgan maktablar
- Interaktiv o'rganish yordamchilari
- O'zbek tilini o'rganish vositalari
4. **Yuqori Throughput Tizimlari**
- 24GB GPU uchun 21 parallel instansiya
- Masshtabdagi API xizmatlari
- Batch qayta ishlash quvurlari
5. **Xarajatlarga Sezgir Joylashtirish**
- Startaplar va kichik bizneslar
- NNT va davlat sektori
- Tadqiqot loyihalari
- Rivojlanayotgan mintaqalar
### ⚠️ Tavsiya Etilmaydi:
- ❌ Professional tarjima xizmatlari (Mistral-Nemo-12B dan foydalaning)
- ❌ Murakkab mulohaza vazifalar (3B+ modellardan foydalaning)
- ❌ Har qanday narxda maksimal sifat (alloma-3B dan foydalaning)
- ❌ Yuqori xavfli qarorlar (tibbiy, huquqiy)
---
## 🔬 O'qitish Tafsilotlari
### Ma'lumotlar To'plami
- **Manba**: [Behbudiy Labs O'zbek Instruct Dataset](https://huggingface.co/behbudiy) (tozalangan versiya)
- **Hajmi**: 162,508 ko'rsatma-javob juftligi
- **Sifat**: Takrorlanmagan, tozalangan, tasdiqlangan
- **Tillar**: O'zbek (kirill va lotin aralashmasi), Ingliz
- **Sohalar**: Suhbat, umumiy bilim, madaniyat, mulohaza, vazifa bajarish
### O'qitish Konfiguratsiyasi
```yaml
base_model: Qwen/Qwen2.5-0.5B-Instruct
method: To'liq fine-tuning (LoRA emas)
trainable_params: 596,049,920 (100%)
optimizer: AdamW
learning_rate: 2e-5
batch_size: 4
gradient_accumulation: 4
effective_batch_size: 16
max_steps: 27,426
early_stopping: checkpoint-26000 (optimal)
warmup_steps: 500
weight_decay: 0.01
max_seq_length: 2048
precision: bfloat16
hardware: NVIDIA RTX 4090 (24GB)
training_time: ~36 soat
framework: Transformers + PyTorch
```
### Nima Uchun To'liq Fine-Tuning (LoRA Emas)?
Biz LoRA yoki lug'at kengaytirishdan ko'ra to'liq fine-tuningni tanladik, chunki:
1. ✅ **Yaxshiroq Sifat**: Yangiliklar tasnifi lug'at kengaytirishdan +318%
2. ✅ **Inference Yuklamasi Yo'q**: LoRA 5-10% kechikish qo'shadi
3. ✅ **Bilimni Saqlaydi**: MMLU ballari saqlanadi (buzilmaydi)
4. ✅ **Ishlab Chiqarish Barqarorligi**: Yagona model fayli, osonroq joylashtirish
5. ✅ **Yaxshiroq Konvergentsiya**: Barcha parametrlarning to'g'ridan-to'g'ri optimizatsiyasi
---
## ⚠️ Cheklovlar
### Ma'lum Muammolar
**1. Q&A Aniqligi Tekshirilmoqda**
- Joriy benchmark 26.7% muvaffaqiyat ko'rsatmoqda (tekshiruv davom etmoqda)
- Oldingi sinovlar 76-100% muvaffaqiyat ko'rsatgan
- Ehtimol chat template qo'llash muammosi
- **Yechim**: O'zingizning maxsus foydalanish holatingizga asoslanib prompt formatini sozlang
**2. Tarjima Sifati Farqi (Kutilgan)**
- BLEU ballari 1B+ modellardan 30-40% pastroq
- 0.6B parametrlar uchun kutilgan cheklov
- **Foydalanish Holati**: Suhbatga e'tibor bering, professional tarjimaga emas
**3. Bilim Kengligi Cheklangan**
- MMLU ~25-37 vs katta modellar uchun 40+
- Hajm bilan cheklangan entsiklopedik bilim
- **Foydalanish Holati**: Suhbat vazifalari, bilim so'rovlari emas
### Mos Emas
- ❌ Professional tarjima xizmatlari
- ❌ Tibbiy/huquqiy/moliyaviy maslahat
- ❌ Yuqori xavfli qaror qabul qilish
- ❌ Murakkab ko'p bosqichli mulohaza
- ❌ Entsiklopedik bilim so'rovlari
### Potentsial Noto'g'riliklar
- Ommaviy o'zbek ma'lumotlarida o'qitilgan (2023-2024)
- Ma'lumotlar to'plamining noto'g'riliklari va cheklovlarini aks ettirishi mumkin
- Mintaqaviy dialektlarga nisbatan standart/shahar o'zbek tilida yaxshiroq
- O'qitish davridan madaniy kontekst surati
---
## 🔄 Versiya Tarixi
### v2.0 (Joriy - Noyabr 2025) ✅ **TAVSIYA ETILADI**
**Checkpoint**: `checkpoint-26000`
**Asosiy O'zgarishlar:**
- ✅ To'liq fine-tuning (596M parametr, 100%)
- ✅ 162,508 tozalangan o'qitish misollari
- ✅ Keng qamrovli benchmarking (6 model)
- ✅ Nol takrorlanish (optimallashtirilgan parametrlar)
- ✅ Ishlab chiqarishga tayyor joylashtirish sinovdan o'tgan
- ✅ Batafsil ishlash tahlili
**Benchmarklar:**
- O'LCHANGAN: 1.12GB VRAM, 5.10s inference, 28.84 tok/s
- BASHORAT: COMET 75-76.5, Sentiment ~61%, News ~45%
---
### v1.0-beta (Sentabr 2025) 🏷️ **ARXIVLANGAN**
**Checkpoint**: `checkpoint-1500`
**Yondashuv:**
- LoRA adapterlari (cheklangan parametr o'qitish)
- O'qitish ma'lumotlarining qismi
- Dastlabki proof-of-concept
**Holat:** v2.0 tomonidan almashtirildi
**Eslatma:** Faqat tarixiy ma'lumot uchun saqlanadi
**Nima Uchun Yangilash:**
- v2.0 da nol takrorlanish (v1.0 da muammolar bor edi)
- Yaxshiroq sifat (to'liq fine-tuning)
- Keng qamrovli benchmarklar
- Ishlab chiqarish sinovidan o'tgan
---
## 📄 Iqtibos
Agar siz bu modelni tadqiqot yoki ishlab chiqarishda ishlatssangiz, iltimos iqtibos keltiring:
```bibtex
@misc{qwen06b-instruct-uz-v2-2025,
author = {Bekhzod Olimov},
title = {Qwen3-0.6B-Instruct-Uz: To'liq Fine-Tuning Orqali Samarali O'zbek Tilini Tushunish},
year = {2025},
month = {Noyabr},
publisher = {HuggingFace},
url = {https://huggingface.co/bekhzod-olimov/Qwen3-0.6B-Instruct-Uz},
note = {162K o'zbek ko'rsatmalarida 596M parametrlarning to'liq fine-tunigi.
Eng samarali o'zbek LLM: 1.12GB VRAM, 5.10s inference.}
}
```
---
## 🙏 Minnatdorchilik
- **[Eldor Fozilov](https://www.linkedin.com/in/eldorfozilov/)** va **[Behbudiy Labs](https://huggingface.co/behbudiy)**: O'zbek ma'lumotlar to'plamini yaratish va o'zbek NLP kashshoflik ishi
- **[Qwen Jamoasi](https://huggingface.co/Qwen)**: A'lo bazaviy model (Qwen2.5-0.5B-Instruct)
- **[HuggingFace](https://huggingface.co/)**: Platforma va jamiyat yordami
- **O'zbek NLP Jamiyati**: Fikr-mulohaza, sinov va doimiy qo'llab-quvvatlash
---
## 📬 Aloqa va Hamkorlik
**Muallif**: Bekhzod Olimov
- 🤗 HuggingFace: [@bekhzod-olimov](https://huggingface.co/bekhzod-olimov)
- 💼 LinkedIn: [Bekhzod Olimov](https://www.linkedin.com/in/bekhzod-olimov/)
- 📧 Email: [Sizning Emailingiz]
- 🐙 GitHub: [Sizning GitHub]
**Ochiq:**
- Tadqiqot hamkorliklari
- Ishlab chiqarish joylashtirish maslahatlari
- Ma'lumotlar to'plami yaxshilanishlari va hissalari
- Benchmark tekshiruvlari
- Jamiyat loyihalari
---
## 🌟 Jamiyat va Qo'llab-quvvatlash
**Xato topdingizmi yoki fikringiz bormi?**
- [Jamiyat tabida](https://huggingface.co/bekhzod-olimov/Qwen3-0.6B-Instruct-Uz/discussions) muammoni oching
- Boshqa foydalanuvchilar bilan muhokamalarga qo'shiling
- Foydalanish holatlaringiz va natijalaringizni baham ko'ring
**Hissa qo'shmoqchimisiz?**
- Haqiqiy ma'lumotlar to'plamlari bilan bashoratlarni tekshirishga yordam bering
- Benchmark to'plamiga hissa qo'shing
- O'qitish ma'lumotlari sifatini yaxshilang
- Darsliklar va misollar yarating
---
## 🔮 Yo'l Xaritasi
### Joriy (v2.0) ✅
- ✅ To'liq fine-tuning tugallandi
- ✅ Keng qamrovli benchmarking
- ✅ Ishlab chiqarish joylashtirish sinovdan o'tdi
- ✅ Ochiq manba reliz
### Yaqinda
- 🔄 INT8 quantization (maqsad: 0.6-0.8GB VRAM)
- 🔄 FLORES-200 tarjima benchmarklari
- 🔄 llama.cpp uchun GGUF formati
- 🔄 Cross-platform joylashtirish uchun ONNX eksport
### Kelajak (Jamiyat So'rovlari)
- Tadqiqot maqolasi (ACL 2025 Workshop ga mo'ljallangan)
- O'qitish qo'llanmasi va yo'riqnomasi
- Maxsus sohalarda fine-tuning
- Multi-modal kengaytmalar (agar jamiyat qiziqish bildirsa)
---
## 📜 Litsenziya
**Apache 2.0** - Tijorat va tadqiqot foydalanish uchun bepul.
To'liq shartlar uchun [LICENSE](LICENSE) ga qarang.
---
## ⭐ Agar Sizga Bu Model Yoqsa
- HuggingFace da ⭐ qo'ying
- Natijalaringiz va foydalanish holatlaringizni baham ko'ring
- Benchmarklar yoki yaxshilanishlarga hissa qo'shing
- Tadqiqot yoki loyihalaringizda iqtibos keltiring
- Yangilanishlar va yangi relizlar uchun kuzatib boring
---
<div align="center">
**🇺🇿 Samaradorlik Orqali O'zbek NLP'ni Demokratlashtirish! 🚀**
*AIni eng muhim joylarda qulay qilish*
[HuggingFace](https://huggingface.co/bekhzod-olimov/Qwen3-0.6B-Instruct-Uz) • [LinkedIn](https://www.linkedin.com/in/bekhzod-olimov/) • [Jamiyat](https://huggingface.co/bekhzod-olimov/Qwen3-0.6B-Instruct-Uz/discussions)
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